Abstract:
Among the evaluation techniques based upon group queries (e.g. focus group), brainstorming does not enjoy particular consideration. This might be the result of its origin and development within organizational and managerial domains, traditionally focused more on “idea production” (and problem solving) than on idea analysis within the context of evaluational and social research. This paper presents a development of classical brainstorming, which is quite useful to evaluation, where the traditional idea-producing step is followed by group analysis and exploration of the shared evaluand-specific semantic space. This evaluational brainstorming is the result of a shared understanding of the evaluand by different stakeholders, who can now ascertain their goals and draw cognitive maps to guide subsequent methodological choices and data gathering requirements.

Abstract:
In this paper PC-VAR estimation of vector autoregressive models (VAR) is proposed. The estimation strategy successfully lessens the curse of dimensionality affecting VAR models, when estimated using sample sizes typically available in quarterly studies. The procedure involves a dynamic regression using a subset of principal components extracted from a vector time series, and the recovery of the implied unrestricted VAR parameter estimates by solving a set of linear constraints. PC-VAR and OLS estimation of unrestricted VAR models show the same asymptotic properties. Monte Carlo results strongly support PC-VAR estimation, yielding gains, in terms of both lower bias and higher efficiency, relatively to OLS estimation of high dimensional unrestricted VAR models in small samples. Guidance for the selection of the number of components to be used in empirical studies is provided.

The solution of an n-dimensional
stochastic differential equation driven by Gaussian white noises is a Markov vector.
In this way, the transition joint probability density function (JPDF) of this
vector is given by a deterministic parabolic partial differential
equation, the so-called Fokker-Planck-Kolmogorov (FPK) equation. There exist few
exact solutions of this equation so that the analyst must resort to approximate
or numerical procedures. The finite element method (FE) is among the latter,
and is reviewed in this paper. Suitable computer codes are written for the two
fundamental versions of the FE method, the Bubnov-Galerkin and the
Petrov-Galerkin method. In order to reduce the computational effort, which is
to reduce the number of nodal points, the following refinements to the method
are proposed: 1) exponential (Gaussian) weighting functions different from the
shape functions are tested; 2) quadratic and cubic splines are used to
interpolate the nodal values that are known in a limited number of points. In
the applications, the transient state is studied for first order systems only,
while for second order systems, the steady-state JPDF is determined, and it is
compared with exact solutions or with simulative solutions: a very good
agreement is found.

In the paper, a
general framework for large scale modeling of macroeconomic and financial time
series is introduced. The proposed approach is characterized by simplicity of
implementation, performing well independently of persistence and
heteroskedasticity properties, accounting for common deterministic and
stochastic factors. Monte Carlo results strongly support the proposed
methodology, validating its use also for relatively small cross-sectional and
temporal samples.

Abstract:
The
separate effects that an electric and a magnetic field would have on the total
energy and spin of an elementary electron state have been computed in a
theoretical quantum field theory framework. It is shown that all the effects in
this process, that are defined “fermion epigenetics”, can be expressed in a
simple and elegant way in terms of the components of the electron field, called
“psinons” in this approach. In the minimal interaction prescription, the
electric and the magnetic effects can be separated into the sum of “classical”
components reproducing conventional Stark and Zeeman effects, and new
components of different type. In the non-relativistic limit, the two residual
effects on the energy only depend on the electron intrinsic properties, i.e. its charge and its spin, and on the
value of the electric and magnetic potentials. A comparison with the results
obtainable in a Pauli formalism approach is discussed and, finally, a very qualitative
calculation of the size of possible effects is performed.

Abstract:
In this article I will show that the victimary theory of René Girard can make a decisive contribution to the interpretation and solution of etymological and semantic questions of the verb “mactare”, which is basic in the terminology related to the topic of the sacrifice. The victimary anthropology provides the hermeneutical key that allows us to consider the expressions “mactare Deum” and “mactare victimam” as genetically synonymous. The theory of René Girard seems to perfectly explain the coexistence, in “mactare victimam”, of two meanings: kill on the one hand, raise and glorify on the other hand. In the case of original lynching, which is mostly hidden in the myth, the victim, which at first is killed for it is believed that the cause of all evils is deified by the persecutors as the cause of all goods. Hence the need to strengthen and renew periodically, through animals or human sacrifices, the saving power of divinity established with the murder of the first victim. That’s why killing and glorifying coexist in “mactare”, which is both “mactare victimam” and “mactare Deum”, since the verb means to kill when referring to a victim and to glorify when referring to God. Indeed, the deification of the victim is the result of his killing.

The paper introduces a new simple semiparametric estimator of the conditional variance-covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step; the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-symultaneous case, i.e. for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple expost procedure to ensure well behaved conditional variance-covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.

The paper introduces a new Frequentist model averaging estimation procedure, based on a stacked OLS estimator across models, implementable on cross-sectional, panel, as well as time series data. The proposed estimator shows the same optimal properties of the OLS estimator under the usual set of assumptions concerning the population regression model. Relatively to available alternative approaches, it has the advantage of performing model averaging exante in a single step, optimally selecting models’ weight according to the MSE metric, i.e. by minimizing the squared Euclidean distance between actual and predicted value vectors. Moreover, it is straightforward to implement, only requiring the estimation of a single OLS augmented regression. By exploiting exante a broader information set and benefiting of more degrees of freedom, the proposed approach yields more accurate and (relatively) more efficient estimation than available expost methods.

Abstract:
A second order oscillator with
nonlinear restoring force and nonlinear damping is considered: it is subject to
both external and internal (parametric) excitations of Gaussian white noise
type. The nonlinearities are chosen in such a way that the associated Fokker-Planck-Kolmogorov
equation is solvable in the steady state. Different choices of some system
parameters give rise to different and interesting shapes of the joint
probability density function of the response, which in some cases appears to be
multimodal. The problem of the determination of the power spectral density of
the response is also addressed by using the true statistical linearization
method.

Abstract:
The main objective of this paper is to analyze the impacts of the concession of tax incentives as a tool for entry promotion in a developing region. The simple model and the numerical example presented indicate that the adoption of tax incentives can cause very important effects. The productive structure could be heavily changed and production could increase improving conditions to consumers that benefit from the larger output and lower prices. Furthermore, the need for strategic action by the government in order to increase the chance of success of their development strategies is also emphasized, especially if one considers that firms often behave strategically.